BBS_neonics_landuse
Olivia Burge
21 January 2015
require(ggthemes)
require(ggplot2)
require(vegan)
require(dplyr)
require(data.table)
require(tidyr)Reading in the new bird data
From Tyson’s csv. Creates a column for the abundance of birds by route (data is by stop).
setwd("/Users/Liv/Documents/NCEAS_GIT/NCEAS-RENCI_2014/BBS_data/")
new_bbs <- read.csv("fifty7_withRTENO.csv")
new_bbs$sum_route_abundance <- rowSums(new_bbs[grep("^Stop[0-9]+", names(new_bbs))])
head(new_bbs)## RouteDataID countrynum statenum Route RPID year AOU Stop1 Stop2 Stop3
## 1 6243648 124 62 16 101 2001 70 0 0 0
## 2 6243648 124 62 16 101 2001 100 0 0 0
## 3 6243648 124 62 16 101 2001 370 0 0 0
## 4 6243648 124 62 16 101 2001 380 0 0 0
## 5 6243648 124 62 16 101 2001 510 0 5 6
## 6 6243648 124 62 16 101 2001 710 46 0 19
## Stop4 Stop5 Stop6 Stop7 Stop8 Stop9 Stop10 Stop11 Stop12 Stop13 Stop14
## 1 0 0 0 0 0 0 0 0 0 0 0
## 2 0 0 0 0 1 0 0 0 0 1 0
## 3 0 0 0 0 0 0 0 1 0 0 0
## 4 0 0 0 0 0 0 0 0 0 0 0
## 5 0 1 1 10 3 1 5 4 0 0 0
## 6 18 0 0 0 0 0 0 0 0 0 0
## Stop15 Stop16 Stop17 Stop18 Stop19 Stop20 Stop21 Stop22 Stop23 Stop24
## 1 0 0 0 0 0 0 0 2 0 0
## 2 0 2 0 2 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0 0 0 0
## 4 0 0 0 0 0 0 0 0 0 0
## 5 0 0 3 4 0 0 4 0 1 0
## 6 0 0 0 0 0 0 0 0 0 0
## Stop25 Stop26 Stop27 Stop28 Stop29 Stop30 Stop31 Stop32 Stop33 Stop34
## 1 0 0 0 0 0 0 0 0 0 0
## 2 0 0 0 3 3 0 0 0 0 0
## 3 0 0 0 0 0 0 0 0 0 0
## 4 0 0 0 0 0 0 0 0 0 0
## 5 4 0 0 1 5 2 0 0 0 0
## 6 0 0 0 0 0 0 0 0 0 0
## Stop35 Stop36 Stop37 Stop38 Stop39 Stop40 Stop41 Stop42 Stop43 Stop44
## 1 0 0 0 0 0 0 0 0 0 0
## 2 0 0 0 0 0 1 0 0 0 0
## 3 0 0 0 0 0 0 0 0 0 0
## 4 0 0 0 0 0 0 0 0 0 0
## 5 2 0 0 0 0 0 0 0 1 1
## 6 0 0 0 0 0 0 0 0 0 0
## Stop45 Stop46 Stop47 Stop48 Stop49 Stop50 RTENO sum_route_abundance
## 1 0 0 0 0 0 1 62016 3
## 2 0 0 0 0 0 0 62016 13
## 3 0 0 0 0 0 0 62016 1
## 4 1 0 0 0 0 0 62016 1
## 5 0 0 0 0 0 1 62016 65
## 6 0 0 0 0 0 0 62016 83names(new_bbs)## [1] "RouteDataID" "countrynum" "statenum"
## [4] "Route" "RPID" "year"
## [7] "AOU" "Stop1" "Stop2"
## [10] "Stop3" "Stop4" "Stop5"
## [13] "Stop6" "Stop7" "Stop8"
## [16] "Stop9" "Stop10" "Stop11"
## [19] "Stop12" "Stop13" "Stop14"
## [22] "Stop15" "Stop16" "Stop17"
## [25] "Stop18" "Stop19" "Stop20"
## [28] "Stop21" "Stop22" "Stop23"
## [31] "Stop24" "Stop25" "Stop26"
## [34] "Stop27" "Stop28" "Stop29"
## [37] "Stop30" "Stop31" "Stop32"
## [40] "Stop33" "Stop34" "Stop35"
## [43] "Stop36" "Stop37" "Stop38"
## [46] "Stop39" "Stop40" "Stop41"
## [49] "Stop42" "Stop43" "Stop44"
## [52] "Stop45" "Stop46" "Stop47"
## [55] "Stop48" "Stop49" "Stop50"
## [58] "RTENO" "sum_route_abundance"new_bbs <- new_bbs[ , c(1:7, 58, 59)] #selects everything except the individual stop abundancesBird trait data
Read in and pare down the bird trait data to the scientific, common names for species and their habitat, nesting, diest, behaviour and conservation status, and 4&6 letter codes.
bird_traits <- read.csv("OH_BBS_1966-2013_traits.csv")
bird_traits <- bird_traits[ , c(1,2,3,4,5,6,7,8,9)]
head(bird_traits[2])## Scientific.name
## 1 Junco hyemalis
## 2 Colaptes auratus
## 3 Empidonax virescens
## 4 Empidonax alnorum
## 5 Botaurus lentiginosus
## 6 Anas rubripesnames(bird_traits) <- toupper(names(bird_traits))
names(bird_traits) <- gsub("[.]", "_", names(bird_traits)) # using the [] to get the period to be used literally. Or can use "fixed = TRUE"
names(bird_traits)## [1] "X4_LETTER_CODE" "SCIENTIFIC_NAME" "X6_LETTER_CODE" "HABITAT"
## [5] "DIET" "NESTING" "BEHAVIOR" "CONSERVATION"
## [9] "COMMON_NAME"bird_traits$SCIENTIFIC_NAME <- toupper(bird_traits$SCIENTIFIC_NAME)
head(bird_traits)## X4_LETTER_CODE SCIENTIFIC_NAME X6_LETTER_CODE HABITAT
## 1 DEJU JUNCO HYEMALIS JUNHYE forest
## 2 NOFL COLAPTES AURATUS COLAUT open_woodland
## 3 ACFL EMPIDONAX VIRESCENS EMPVIR forest
## 4 ALFL EMPIDONAX ALNORUM EMPALN scrub
## 5 AMBI BOTAURUS LENTIGINOSUS BOTLEN marsh
## 6 ABDU ANAS RUBRIPES ANARUB lake_pond
## DIET NESTING BEHAVIOR CONSERVATION
## 1 seeds ground ground_forager LC
## 2 insects cavity ground_forager LC
## 3 insects tree fly_catching LC
## 4 insects shrub fly_catching LC
## 5 fish ground stalking LC
## 6 insects ground dabbler LC
## COMMON_NAME
## 1 (Slate-colored Junco) Dark-eyed Junco
## 2 (Yellow-shafted Flicker) Northern Flicker
## 3 Acadian Flycatcher
## 4 Alder Flycatcher
## 5 American Bittern
## 6 American Black DuckBird code AOU data
BBS data comes with a different kind of AOU code. This chunk tidies up the AOU code dataframe and makes the common and scienitific names compatible with the BBS data. Need to use the common names, scientific names are slightly off in some cases.
AOU_codes <- read.csv("raw_data/AOU_codes.csv") ## a cleaned up (ie header removed, split into columns - no substantive changes to any species names or column headers) version of: ftp://ftpext.usgs.gov/pub/er/md/laurel/BBS/DataFiles/SpeciesList.txt
head(AOU_codes)## Seq AOU English_Common_Name French_Common_Name
## 1 4 10010 Great Tinamou Grand Tinamou
## 2 7 10030 Little Tinamou Tinamou soui
## 3 10 40080 Thicket Tinamou Tinamou cannelle
## 4 13 10050 Slaty-breasted Tinamou Tinamou de Boucard
## 5 16 1770 Black-bellied Whistling-Duck Dendrocygne \xe0 ventre noir
## 6 19 10200 West Indian Whistling-Duck Dendrocygne des Antilles
## Spanish_Common_Name ORDER Family Genus Species
## 1 Tinamus major Tinamiformes Tinamidae Tinamus major
## 2 Crypturellus soui Tinamiformes Tinamidae Crypturellus soui
## 3 Crypturellus cinnamomeus Tinamiformes Tinamidae Crypturellus cinnamomeus
## 4 Crypturellus boucardi Tinamiformes Tinamidae Crypturellus boucardi
## 5 Dendrocygna autumnalis Anseriformes Anatidae Dendrocygna autumnalis
## 6 Dendrocygna arborea Anseriformes Anatidae Dendrocygna arboreaAOU_codes$common_name <- toupper(AOU_codes$English_Common_Name)
names(AOU_codes)## [1] "Seq" "AOU" "English_Common_Name"
## [4] "French_Common_Name" "Spanish_Common_Name" "ORDER"
## [7] "Family" "Genus" "Species"
## [10] "common_name"AOU_codes <- AOU_codes[ , c(2, 10, 6:9)]
names(AOU_codes) <- toupper(names(AOU_codes))
head(AOU_codes)## AOU COMMON_NAME ORDER FAMILY GENUS
## 1 10010 GREAT TINAMOU Tinamiformes Tinamidae Tinamus
## 2 10030 LITTLE TINAMOU Tinamiformes Tinamidae Crypturellus
## 3 40080 THICKET TINAMOU Tinamiformes Tinamidae Crypturellus
## 4 10050 SLATY-BREASTED TINAMOU Tinamiformes Tinamidae Crypturellus
## 5 1770 BLACK-BELLIED WHISTLING-DUCK Anseriformes Anatidae Dendrocygna
## 6 10200 WEST INDIAN WHISTLING-DUCK Anseriformes Anatidae Dendrocygna
## SPECIES
## 1 major
## 2 soui
## 3 cinnamomeus
## 4 boucardi
## 5 autumnalis
## 6 arboreaAOU_codes$SCIENTIFIC_NAME <- paste(AOU_codes$GENUS, AOU_codes$SPECIES, sep = " ")
AOU_codes$SCIENTIFIC_NAME <- toupper(AOU_codes$SCIENTIFIC_NAME)
#testing that all our traits at leat are in the new AOU codes:
bird_traits$SCIENTIFIC_NAME %in% AOU_codes$SCIENTIFIC_NAME # = not all true.## [1] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
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## [1464] FALSEhead(bird_traits)## X4_LETTER_CODE SCIENTIFIC_NAME X6_LETTER_CODE HABITAT
## 1 DEJU JUNCO HYEMALIS JUNHYE forest
## 2 NOFL COLAPTES AURATUS COLAUT open_woodland
## 3 ACFL EMPIDONAX VIRESCENS EMPVIR forest
## 4 ALFL EMPIDONAX ALNORUM EMPALN scrub
## 5 AMBI BOTAURUS LENTIGINOSUS BOTLEN marsh
## 6 ABDU ANAS RUBRIPES ANARUB lake_pond
## DIET NESTING BEHAVIOR CONSERVATION
## 1 seeds ground ground_forager LC
## 2 insects cavity ground_forager LC
## 3 insects tree fly_catching LC
## 4 insects shrub fly_catching LC
## 5 fish ground stalking LC
## 6 insects ground dabbler LC
## COMMON_NAME
## 1 (Slate-colored Junco) Dark-eyed Junco
## 2 (Yellow-shafted Flicker) Northern Flicker
## 3 Acadian Flycatcher
## 4 Alder Flycatcher
## 5 American Bittern
## 6 American Black Duckbird_traits$COMMON_NAME %in% AOU_codes$English_Common_Name # = all true!## [1] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
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## [199] FALSE FALSE FALSE FALSE FALSE FALSE FALSEAOU_codes$English_Common_Name %in% bird_traits$COMMON_NAME # many many falses## logical(0)unique(new_bbs$AOU) %in% unique(AOU_codes$AOU) # good.## [1] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
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## [379] TRUE TRUE TRUE TRUE# merge the AOU codes into the ohio dataMerging AOU codes and traits
Putting the trait and AOU codes together -
head(AOU_codes)## AOU COMMON_NAME ORDER FAMILY GENUS
## 1 10010 GREAT TINAMOU Tinamiformes Tinamidae Tinamus
## 2 10030 LITTLE TINAMOU Tinamiformes Tinamidae Crypturellus
## 3 40080 THICKET TINAMOU Tinamiformes Tinamidae Crypturellus
## 4 10050 SLATY-BREASTED TINAMOU Tinamiformes Tinamidae Crypturellus
## 5 1770 BLACK-BELLIED WHISTLING-DUCK Anseriformes Anatidae Dendrocygna
## 6 10200 WEST INDIAN WHISTLING-DUCK Anseriformes Anatidae Dendrocygna
## SPECIES SCIENTIFIC_NAME
## 1 major TINAMUS MAJOR
## 2 soui CRYPTURELLUS SOUI
## 3 cinnamomeus CRYPTURELLUS CINNAMOMEUS
## 4 boucardi CRYPTURELLUS BOUCARDI
## 5 autumnalis DENDROCYGNA AUTUMNALIS
## 6 arborea DENDROCYGNA ARBOREAhead(new_bbs)## RouteDataID countrynum statenum Route RPID year AOU RTENO
## 1 6243648 124 62 16 101 2001 70 62016
## 2 6243648 124 62 16 101 2001 100 62016
## 3 6243648 124 62 16 101 2001 370 62016
## 4 6243648 124 62 16 101 2001 380 62016
## 5 6243648 124 62 16 101 2001 510 62016
## 6 6243648 124 62 16 101 2001 710 62016
## sum_route_abundance
## 1 3
## 2 13
## 3 1
## 4 1
## 5 65
## 6 83AOU_codes$COMMON_NAME <- AOU_codes$English_Common_Name
traits_aou <- inner_join(bird_traits, AOU_codes)## Joining by: "SCIENTIFIC_NAME"BBS_traits <- inner_join(new_bbs, traits_aou)## Joining by: "AOU"names(BBS_traits)## [1] "AOU" "RouteDataID" "countrynum"
## [4] "statenum" "Route" "RPID"
## [7] "year" "RTENO" "sum_route_abundance"
## [10] "SCIENTIFIC_NAME" "X4_LETTER_CODE" "X6_LETTER_CODE"
## [13] "HABITAT" "DIET" "NESTING"
## [16] "BEHAVIOR" "CONSERVATION" "COMMON_NAME"
## [19] "ORDER" "FAMILY" "GENUS"
## [22] "SPECIES"Neonics data by route
Based on Tyson’s GIS analysis.
neonics_buffers <- read.csv("../Pesticides/pest_buff_overtime.csv")
head(neonics_buffers)## YEAR RTENO buffer COMPOUND high_kg_buff low_kg_buff ag_pix_buff
## 1 1992 66085 1000 ATRAZINE 859.34 847.89 52707
## 2 1992 66085 1000 GLYPHOSATE 108.95 77.76 52707
## 3 1992 66085 10000 ATRAZINE 8348.23 8223.01 502193
## 4 1992 66085 10000 GLYPHOSATE 1075.86 795.61 502193
## 5 1992 66085 200 ATRAZINE 181.23 178.84 11132
## 6 1992 66085 200 GLYPHOSATE 22.95 16.33 11132Merge BBS_traits and neonics
sort(unique(neonics_buffers$YEAR))## [1] 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005
## [15] 2006 2007 2008 2009 2010 2011names(BBS_traits) <- toupper(names(BBS_traits))
sort(unique(BBS_traits$YEAR))## [1] 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008
## [15] 2009 2010 2011 2012 2013Therefore, we need to select the period 1995 - 2011 for merging.
BBS_neonics <- inner_join(BBS_traits[ BBS_traits$YEAR < 2012, ], neonics_buffers[ neonics_buffers$YEAR > 1994, ]) # joins by RTENO and YEAR which works. ## Joining by: c("YEAR", "RTENO")Landuse data by route
Based on Tyson’s GIS analysis. Notes for the csv: reclass is Anderson Level I land cover: (0=nodata, 1=water, 2=developed, 3=barren, 4=forest, 5=grassland/shrub, 6=agriculture, 7=wetlands). total is the number of pixels (multiply by 0.009 to get square km).
landuse_buffers <- read.csv("../Pesticides/LC_buffers_overtime.csv")
head(landuse_buffers)## BBS_route reclass buffer YEAR total_pix km2 RTENO
## 1 2310 1 1000 1992 7 0.0063 2310
## 2 2310 2 1000 1992 7447 6.7023 2310
## 3 2310 3 1000 1992 106 0.0954 2310
## 4 2310 6 1000 1992 70656 63.5904 2310
## 5 2310 7 1000 1992 1155 1.0395 2310
## 6 2310 1 10000 1992 2435 2.1915 2310reclass_table <- data.frame("reclass" = 0:7, "land_cover" = c("nodata", "water", "developed", "barren", "forest", "grassland_shrub", "agriculture", "wetlands"))
landuse_buffers <- merge(landuse_buffers, reclass_table, by = "reclass")
class(landuse_buffers$land_cover)## [1] "factor"head(landuse_buffers)## reclass BBS_route buffer YEAR total_pix km2 RTENO land_cover
## 1 1 2310 1000 1992 7.0 0.0063 2310 water
## 2 1 2338 400 1996 229.8 0.2068 66049 water
## 3 1 2322 1000 1993 205.1 0.1846 66022 water
## 4 1 2359 2000 1992 1826.0 1.6434 66113 water
## 5 1 2371 400 1994 210.9 0.1898 66188 water
## 6 1 2310 10000 1992 2435.0 2.1915 2310 watertail(landuse_buffers)## reclass BBS_route buffer YEAR total_pix km2 RTENO land_cover
## 56235 7 2317 2000 1993 4806.2 4.3256 66011 wetlands
## 56236 7 2334 2000 2006 593.0 0.5337 66042 wetlands
## 56237 7 2367 400 1996 0.0 0.0000 66165 wetlands
## 56238 7 2312 400 2000 1560.4 1.4044 66002 wetlands
## 56239 7 2337 2000 2000 460.9 0.4148 66047 wetlands
## 56240 7 2372 2000 2005 116.0 0.1044 66251 wetlandsnames(landuse_buffers)## [1] "reclass" "BBS_route" "buffer" "YEAR" "total_pix"
## [6] "km2" "RTENO" "land_cover"landuse_buffers2 <- landuse_buffers[c(2, 3, 4, 5, 7, 8)]
names(landuse_buffers2)## [1] "BBS_route" "buffer" "YEAR" "total_pix" "RTENO"
## [6] "land_cover"class(landuse_buffers2$total_pix)## [1] "numeric"str(landuse_buffers2)## 'data.frame': 56240 obs. of 6 variables:
## $ BBS_route : int 2310 2338 2322 2359 2371 2310 2322 2346 2326 2371 ...
## $ buffer : int 1000 400 1000 2000 400 10000 10000 2000 10000 5000 ...
## $ YEAR : int 1992 1996 1993 1992 1994 1992 1993 2010 1995 1994 ...
## $ total_pix : num 7 230 205 1826 211 ...
## $ RTENO : int 2310 66049 66022 66113 66188 2310 66022 66073 66027 66188 ...
## $ land_cover: Factor w/ 8 levels "agriculture",..: 7 7 7 7 7 7 7 7 7 7 ...head(landuse_buffers2)## BBS_route buffer YEAR total_pix RTENO land_cover
## 1 2310 1000 1992 7.0 2310 water
## 2 2338 400 1996 229.8 66049 water
## 3 2322 1000 1993 205.1 66022 water
## 4 2359 2000 1992 1826.0 66113 water
## 5 2371 400 1994 210.9 66188 water
## 6 2310 10000 1992 2435.0 2310 waterlanduse_buffers_wide <- spread(landuse_buffers2, land_cover, total_pix)
tail(landuse_buffers_wide, 50)## BBS_route buffer YEAR RTENO agriculture barren developed forest
## 8351 2379 2000 2002 66907 17962 NA 5161 63955
## 8352 2379 2000 2003 66907 17968 NA 5161 63949
## 8353 2379 2000 2004 66907 17975 NA 5161 63942
## 8354 2379 2000 2005 66907 17981 NA 5161 63936
## 8355 2379 2000 2006 66907 17988 NA 5161 63929
## 8356 2379 2000 2007 66907 17990 NA 5161 63927
## 8357 2379 2000 2008 66907 17992 NA 5161 63925
## 8358 2379 2000 2009 66907 17993 NA 5161 63923
## 8359 2379 2000 2010 66907 17995 NA 5161 63921
## 8360 2379 2000 2011 66907 17997 NA 5161 63919
## 8361 2379 5000 1992 66907 78416 0.00 17636 137612
## 8362 2379 5000 1993 66907 78231 0.00 17635 137774
## 8363 2379 5000 1994 66907 78046 0.00 17634 137936
## 8364 2379 5000 1995 66907 77861 0.00 17634 138097
## 8365 2379 5000 1996 66907 77676 0.00 17633 138259
## 8366 2379 5000 1997 66907 77491 0.00 17632 138421
## 8367 2379 5000 1998 66907 77306 0.00 17631 138583
## 8368 2379 5000 1999 66907 77121 0.00 17631 138744
## 8369 2379 5000 2000 66907 76936 0.00 17630 138906
## 8370 2379 5000 2001 66907 76751 13.00 17629 139068
## 8371 2379 5000 2002 66907 76758 13.00 17629 139061
## 8372 2379 5000 2003 66907 76764 13.00 17629 139055
## 8373 2379 5000 2004 66907 76771 13.00 17629 139048
## 8374 2379 5000 2005 66907 76777 13.00 17629 139042
## 8375 2379 5000 2006 66907 76784 13.00 17629 139035
## 8376 2379 5000 2007 66907 76805 13.00 17636 139003
## 8377 2379 5000 2008 66907 76827 13.00 17642 138971
## 8378 2379 5000 2009 66907 76848 13.00 17649 138938
## 8379 2379 5000 2010 66907 76870 13.00 17655 138906
## 8380 2379 5000 2011 66907 76891 50.00 17662 138874
## 8381 2379 10000 1992 66907 240727 48.00 43000 326098
## 8382 2379 10000 1993 66907 240348 44.78 42997 326434
## 8383 2379 10000 1994 66907 239969 41.56 42994 326770
## 8384 2379 10000 1995 66907 239589 38.33 42990 327106
## 8385 2379 10000 1996 66907 239210 35.11 42987 327442
## 8386 2379 10000 1997 66907 238831 31.89 42984 327778
## 8387 2379 10000 1998 66907 238452 28.67 42981 328114
## 8388 2379 10000 1999 66907 238072 25.44 42977 328450
## 8389 2379 10000 2000 66907 237693 22.22 42974 328786
## 8390 2379 10000 2001 66907 237314 24.00 42971 329122
## 8391 2379 10000 2002 66907 237320 24.00 42971 329111
## 8392 2379 10000 2003 66907 237327 24.00 42971 329101
## 8393 2379 10000 2004 66907 237333 24.00 42971 329090
## 8394 2379 10000 2005 66907 237340 24.00 42971 329080
## 8395 2379 10000 2006 66907 237346 24.00 42971 329069
## 8396 2379 10000 2007 66907 237462 31.40 42987 328941
## 8397 2379 10000 2008 66907 237577 38.80 43003 328813
## 8398 2379 10000 2009 66907 237693 46.20 43018 328685
## 8399 2379 10000 2010 66907 237808 53.60 43034 328557
## 8400 2379 10000 2011 66907 237924 61.00 43050 328429
## grassland_shrub water wetlands
## 8351 822 3285 144.0
## 8352 822 3285 144.0
## 8353 822 3285 144.0
## 8354 822 3285 144.0
## 8355 822 3285 144.0
## 8356 822 3285 144.2
## 8357 822 3285 144.4
## 8358 822 3285 144.6
## 8359 822 3285 144.8
## 8360 822 3285 145.0
## 8361 2307 4445 121.0
## 8362 2318 4457 121.8
## 8363 2329 4469 122.6
## 8364 2340 4482 123.3
## 8365 2351 4494 124.1
## 8366 2362 4506 124.9
## 8367 2373 4518 125.7
## 8368 2384 4531 126.4
## 8369 2395 4543 127.2
## 8370 2490 4578 611.0
## 8371 2490 4578 611.0
## 8372 2490 4578 611.0
## 8373 2490 4578 611.0
## 8374 2490 4578 611.0
## 8375 2490 4578 611.0
## 8376 2482 4580 613.6
## 8377 2474 4583 616.2
## 8378 2465 4585 618.8
## 8379 2457 4588 621.4
## 8380 2449 4590 624.0
## 8381 6283 6532 752.0
## 8382 6310 6550 756.4
## 8383 6338 6568 760.9
## 8384 6365 6585 765.3
## 8385 6393 6603 769.8
## 8386 6420 6621 774.2
## 8387 6448 6639 778.7
## 8388 6475 6656 783.1
## 8389 6503 6674 787.6
## 8390 6530 6692 1990.0
## 8391 6530 6700 1986.0
## 8392 6530 6708 1982.0
## 8393 6530 6717 1978.0
## 8394 6530 6725 1974.0
## 8395 6530 6733 1970.0
## 8396 6503 6742 1976.8
## 8397 6476 6752 1983.6
## 8398 6449 6761 1990.4
## 8399 6422 6771 1997.2
## 8400 6395 6780 2004.0names(landuse_buffers_wide)## [1] "BBS_route" "buffer" "YEAR"
## [4] "RTENO" "agriculture" "barren"
## [7] "developed" "forest" "grassland_shrub"
## [10] "water" "wetlands"Merging BBS_neonics with landuse
sort(unique(landuse_buffers$YEAR)) # 1992 - 2011## [1] 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005
## [15] 2006 2007 2008 2009 2010 2011BBS_ln <- inner_join(BBS_neonics, landuse_buffers_wide[ landuse_buffers_wide$YEAR > 1994, ])## Joining by: c("YEAR", "RTENO", "buffer")# joins by: c("YEAR", "RTENO", "BBS_route", "buffer")
tail(BBS_ln)## YEAR RTENO buffer AOU ROUTEDATAID COUNTRYNUM STATENUM ROUTE RPID
## 1429884 2002 66907 2000 7660 6245008 840 66 907 101
## 1429885 2002 66907 400 7660 6245008 840 66 907 101
## 1429886 2002 66907 400 7660 6245008 840 66 907 101
## 1429887 2002 66907 400 7660 6245008 840 66 907 101
## 1429888 2002 66907 400 7660 6245008 840 66 907 101
## 1429889 2002 66907 400 7660 6245008 840 66 907 101
## SUM_ROUTE_ABUNDANCE SCIENTIFIC_NAME X4_LETTER_CODE X6_LETTER_CODE
## 1429884 3 SIALIA SIALIS EABL SIASIA
## 1429885 3 SIALIA SIALIS EABL SIASIA
## 1429886 3 SIALIA SIALIS EABL SIASIA
## 1429887 3 SIALIA SIALIS EABL SIASIA
## 1429888 3 SIALIA SIALIS EABL SIASIA
## 1429889 3 SIALIA SIALIS EABL SIASIA
## HABITAT DIET NESTING BEHAVIOR CONSERVATION
## 1429884 grassland insects cavity ground_forager LC
## 1429885 grassland insects cavity ground_forager LC
## 1429886 grassland insects cavity ground_forager LC
## 1429887 grassland insects cavity ground_forager LC
## 1429888 grassland insects cavity ground_forager LC
## 1429889 grassland insects cavity ground_forager LC
## COMMON_NAME ORDER FAMILY GENUS SPECIES
## 1429884 Eastern Bluebird Passeriformes Turdidae Sialia sialis
## 1429885 Eastern Bluebird Passeriformes Turdidae Sialia sialis
## 1429886 Eastern Bluebird Passeriformes Turdidae Sialia sialis
## 1429887 Eastern Bluebird Passeriformes Turdidae Sialia sialis
## 1429888 Eastern Bluebird Passeriformes Turdidae Sialia sialis
## 1429889 Eastern Bluebird Passeriformes Turdidae Sialia sialis
## COMPOUND high_kg_buff low_kg_buff ag_pix_buff BBS_route
## 1429884 THIAMETHOXAM 2.265e-04 2.265e-04 2186 2379
## 1429885 ATRAZINE 2.287e+01 2.269e+01 1470 2379
## 1429886 FIPRONIL 3.360e-02 NA 1470 2379
## 1429887 GLYPHOSATE 3.253e+01 3.199e+01 1470 2379
## 1429888 IMIDACLOPRID 2.421e-02 2.421e-02 1470 2379
## 1429889 THIAMETHOXAM 6.218e-07 6.218e-07 6 2379
## agriculture barren developed forest grassland_shrub water wetlands
## 1429884 17962 NA 5161 63955 822 3285 144
## 1429885 1463 NA 1786 24349 300 740 17
## 1429886 1463 NA 1786 24349 300 740 17
## 1429887 1463 NA 1786 24349 300 740 17
## 1429888 1463 NA 1786 24349 300 740 17
## 1429889 1463 NA 1786 24349 300 740 17Maybe a baby model?
names(BBS_ln)## [1] "YEAR" "RTENO" "buffer"
## [4] "AOU" "ROUTEDATAID" "COUNTRYNUM"
## [7] "STATENUM" "ROUTE" "RPID"
## [10] "SUM_ROUTE_ABUNDANCE" "SCIENTIFIC_NAME" "X4_LETTER_CODE"
## [13] "X6_LETTER_CODE" "HABITAT" "DIET"
## [16] "NESTING" "BEHAVIOR" "CONSERVATION"
## [19] "COMMON_NAME" "ORDER" "FAMILY"
## [22] "GENUS" "SPECIES" "COMPOUND"
## [25] "high_kg_buff" "low_kg_buff" "ag_pix_buff"
## [28] "BBS_route" "agriculture" "barren"
## [31] "developed" "forest" "grassland_shrub"
## [34] "water" "wetlands"unique(BBS_ln$buffer)## [1] 1000 10000 200 2000 400 5000BBS_ln$agriculture_scaled <- scale(BBS_ln$agriculture)
BBS_ln$high_kg_buff_scaled <- scale(BBS_ln$high_kg_buff)
head(BBS_ln)## YEAR RTENO buffer AOU ROUTEDATAID COUNTRYNUM STATENUM ROUTE RPID
## 1 1997 66001 1000 1320 6228264 840 66 1 101
## 2 1997 66001 1000 1320 6228264 840 66 1 101
## 3 1997 66001 1000 1320 6228264 840 66 1 101
## 4 1997 66001 10000 1320 6228264 840 66 1 101
## 5 1997 66001 10000 1320 6228264 840 66 1 101
## 6 1997 66001 10000 1320 6228264 840 66 1 101
## SUM_ROUTE_ABUNDANCE SCIENTIFIC_NAME X4_LETTER_CODE X6_LETTER_CODE
## 1 2 ANAS PLATYRHYNCHOS MALL ANAPLA
## 2 2 ANAS PLATYRHYNCHOS MALL ANAPLA
## 3 2 ANAS PLATYRHYNCHOS MALL ANAPLA
## 4 2 ANAS PLATYRHYNCHOS MALL ANAPLA
## 5 2 ANAS PLATYRHYNCHOS MALL ANAPLA
## 6 2 ANAS PLATYRHYNCHOS MALL ANAPLA
## HABITAT DIET NESTING BEHAVIOR CONSERVATION COMMON_NAME ORDER
## 1 lake_pond seeds ground dabbler LC Mallard Anseriformes
## 2 lake_pond seeds ground dabbler LC Mallard Anseriformes
## 3 lake_pond seeds ground dabbler LC Mallard Anseriformes
## 4 lake_pond seeds ground dabbler LC Mallard Anseriformes
## 5 lake_pond seeds ground dabbler LC Mallard Anseriformes
## 6 lake_pond seeds ground dabbler LC Mallard Anseriformes
## FAMILY GENUS SPECIES COMPOUND high_kg_buff low_kg_buff
## 1 Anatidae Anas platyrhynchos ATRAZINE NA NA
## 2 Anatidae Anas platyrhynchos GLYPHOSATE NA NA
## 3 Anatidae Anas platyrhynchos IMIDACLOPRID NA NA
## 4 Anatidae Anas platyrhynchos ATRAZINE NA NA
## 5 Anatidae Anas platyrhynchos GLYPHOSATE NA NA
## 6 Anatidae Anas platyrhynchos IMIDACLOPRID NA NA
## ag_pix_buff BBS_route agriculture barren developed forest
## 1 NA 2311 75241 0 7387 0
## 2 NA 2311 75241 0 7387 0
## 3 NA 2311 75241 0 7387 0
## 4 NA 2311 997407 1057 96264 24625
## 5 NA 2311 997407 1057 96264 24625
## 6 NA 2311 997407 1057 96264 24625
## grassland_shrub water wetlands agriculture_scaled high_kg_buff_scaled
## 1 0 13.44 0 -0.3525 NA
## 2 0 13.44 0 -0.3525 NA
## 3 0 13.44 0 -0.3525 NA
## 4 8417 8353.56 1241 3.5449 NA
## 5 8417 8353.56 1241 3.5449 NA
## 6 8417 8353.56 1241 3.5449 NA# test_mod2 <- glmer(data = BBS_ln[ BBS_ln$buffer == 200, ],
# family = poisson,
# formula= SUM_ROUTE_ABUNDANCE ~ agriculture_scaled + high_kg_buff_scaled:COMPOUND + DIET + (1|BBS_route) + (1|YEAR))
#summary(test_mod2)
require(coefplot2)
#coefplot2(test_mod2)
require(dismo)## Loading required package: dismo
## Loading required package: raster
## Loading required package: sp
##
## Attaching package: 'raster'
##
## The following object is masked from 'package:tidyr':
##
## extract
##
## The following object is masked from 'package:dplyr':
##
## selectBBS_200 <- BBS_ln[ BBS_ln$buffer == 200, ]
names(BBS_200)## [1] "YEAR" "RTENO" "buffer"
## [4] "AOU" "ROUTEDATAID" "COUNTRYNUM"
## [7] "STATENUM" "ROUTE" "RPID"
## [10] "SUM_ROUTE_ABUNDANCE" "SCIENTIFIC_NAME" "X4_LETTER_CODE"
## [13] "X6_LETTER_CODE" "HABITAT" "DIET"
## [16] "NESTING" "BEHAVIOR" "CONSERVATION"
## [19] "COMMON_NAME" "ORDER" "FAMILY"
## [22] "GENUS" "SPECIES" "COMPOUND"
## [25] "high_kg_buff" "low_kg_buff" "ag_pix_buff"
## [28] "BBS_route" "agriculture" "barren"
## [31] "developed" "forest" "grassland_shrub"
## [34] "water" "wetlands" "agriculture_scaled"
## [37] "high_kg_buff_scaled"class(BBS_200$SPECIES)## [1] "factor"# gbm_neonic <- gbm.step(data = BBS_200,
# gbm.x=c(1, 3, 15, 16, 17, 18, 23, 25, 26, 29:35),
# gbm.y = 11, family = "poisson",
# tree.complexity = 14) #,
# #step.size = 20, n.trees = 50,
# #learning.rate = 0.001, bag.fraction = 0.75)
# summary(gbm_neonic)
#
# glmm_neonic <- glmer(data = BBS_200,
# formula = SUM_ROUTE_ABUNDANCE ~ high_kg_buff:COMPOUND +
# high_kg_buff:forest:COMPOUND +
# high_kg_buff:agriculture:COMPOUND +
# forest + agriculture +
# (1|GENUS) + (1|YEAR),
# family = "poisson")
#
# summary(glmm_neonic)
# coefplot2(glmm_neonic)
#
# neonic_int<-gbm.interactions(gbm_neonic)
# neonic_int$interactions
#
# gbm.perspec(gbm_neonic, 1,3)
# gbm.perspec(gbm_neonic, 2,3)
# gbm.perspec(gbm_neonic, 4,8)
# gbm.plot(gbm_neonic, 3,4)
#
# save.image(file = "big_gbm.Rdata")
# load(file = "big_gbm.Rdata")
#saveRDS(gbm_neonic, file = "big_serialised_gbm.rds")Some exploration!
head(landuse_buffers)## reclass BBS_route buffer YEAR total_pix km2 RTENO land_cover
## 1 1 2310 1000 1992 7.0 0.0063 2310 water
## 2 1 2338 400 1996 229.8 0.2068 66049 water
## 3 1 2322 1000 1993 205.1 0.1846 66022 water
## 4 1 2359 2000 1992 1826.0 1.6434 66113 water
## 5 1 2371 400 1994 210.9 0.1898 66188 water
## 6 1 2310 10000 1992 2435.0 2.1915 2310 waterggplot(landuse_buffers[landuse_buffers$land_cover %in% c("forest", "agriculture", "developed", "grassland_shrub") & landuse_buffers$buffer == 400, ], aes(x = YEAR, y = total_pix)) +
geom_smooth(aes(colour = land_cover)) +
# geom_density2d(aes(colour = land_cover)) +
geom_point(aes(colour = land_cover), alpha = 0.3, position = position_jitter(width = 0.3))+
theme_bw()+
scale_colour_colorblind("Land Cover", labels = c("Agriculture", "Developed", "Forest", "Grass/Shrub"))+
guides(colour=guide_legend(override.aes=list(fill=NA)))+
theme(legend.position = "bottom", legend.key = element_blank(), legend.background = element_rect(colour = "grey"))## geom_smooth: method="auto" and size of largest group is >=1000, so using gam with formula: y ~ s(x, bs = "cs"). Use 'method = x' to change the smoothing method.
ggplot(landuse_buffers[landuse_buffers$land_cover %in% c("forest", "agriculture", "developed", "grassland_shrub") & landuse_buffers$buffer == 400, ], aes(x = YEAR, y = total_pix)) +
geom_smooth(aes(colour = land_cover)) +
geom_density2d(aes(colour = land_cover)) +
#geom_point(aes(colour = land_cover), alpha = 0.3, position = position_jitter(width = 0.3))+
theme_bw()+
scale_colour_colorblind("Land Cover", labels = c("Agriculture", "Developed", "Forest", "Grass/Shrub"))+
guides(colour = FALSE) +
facet_wrap(~ land_cover, scales = "free")## geom_smooth: method="auto" and size of largest group is >=1000, so using gam with formula: y ~ s(x, bs = "cs"). Use 'method = x' to change the smoothing method.
## geom_smooth: method="auto" and size of largest group is >=1000, so using gam with formula: y ~ s(x, bs = "cs"). Use 'method = x' to change the smoothing method.
## geom_smooth: method="auto" and size of largest group is >=1000, so using gam with formula: y ~ s(x, bs = "cs"). Use 'method = x' to change the smoothing method.
## geom_smooth: method="auto" and size of largest group is >=1000, so using gam with formula: y ~ s(x, bs = "cs"). Use 'method = x' to change the smoothing method.
ggplot(landuse_buffers[landuse_buffers$land_cover %in% c("forest", "agriculture", "developed", "grassland_shrub") & landuse_buffers$buffer == 400 & landuse_buffers$YEAR %in% c(1995, 2000, 2005, 2010), ], aes(x = land_cover, y = total_pix)) +
geom_violin(aes(fill = land_cover))+
theme_bw()+
scale_fill_colorblind("Land Cover", labels = c("Agriculture", "Developed", "Forest", "Grass/Shrub")) +
facet_wrap(~YEAR)
ggplot(landuse_buffers[landuse_buffers$land_cover %in% c("forest", "agriculture", "developed", "grassland_shrub") & landuse_buffers$buffer == 400 & landuse_buffers$YEAR %in% c(1995, 2000, 2005, 2010), ], aes(x = factor(YEAR), y = total_pix)) +
geom_violin(aes(fill = land_cover))+
theme_bw()+
scale_fill_colorblind("Land Cover", labels = c("Agriculture", "Developed", "Forest", "Grass/Shrub")) +
guides(fill = FALSE)+
facet_wrap(~land_cover, scales = "free")
summary_landuse <- landuse_buffers[landuse_buffers$land_cover %in% c("forest", "agriculture", "developed", "grassland_shrub"), ] %>% group_by(YEAR, land_cover, buffer) %>% filter(buffer == 400) %>% summarise(total_pix = sum(total_pix))
ggplot(summary_landuse, aes(x = YEAR, y = total_pix)) +
geom_area(aes(fill = land_cover, colour = land_cover), position = "fill")+
theme_bw()+
scale_fill_manual("Land Cover", values = c("sienna4", "gray20", "forestgreen", "orange"), labels = c("Agriculture", "Developed", "Forest", "Grass/Shrub")) +
scale_colour_manual("Land Cover", values = c("sienna4", "gray20", "forestgreen", "orange"), labels = c("Agriculture", "Developed", "Forest", "Grass/Shrub")) +
theme(legend.position = "bottom", legend.background = element_rect(colour = "grey"))
unique(landuse_buffers$buffer)## [1] 1000 400 2000 10000 5000 200summary_landuse3 <- landuse_buffers[landuse_buffers$land_cover %in% c("forest", "agriculture", "developed", "grassland_shrub"), ] %>% group_by(YEAR, land_cover, buffer) %>% summarise(total_pix = sum(total_pix))
ggplot(summary_landuse3, aes(x = YEAR, y = total_pix)) +
geom_area(aes(fill = land_cover, colour = land_cover), position = "fill")+
theme_bw()+
scale_fill_manual("Land Cover", values = c("sienna4", "gray20", "forestgreen", "orange"), labels = c("Agriculture", "Developed", "Forest", "Grass/Shrub")) +
scale_colour_manual("Land Cover", values = c("sienna4", "gray20", "forestgreen", "orange"), labels = c("Agriculture", "Developed", "Forest", "Grass/Shrub")) +
theme(legend.position = "bottom", legend.background = element_rect(colour = "grey")) +
facet_wrap(~buffer)
# summary_landuse2 <- landuse_buffers%>% group_by(YEAR, land_cover, buffer) %>% filter(buffer == 400) %>% summarise(total_pix = sum(total_pix))
#
# ggplot(summary_landuse2, aes(x = YEAR, y = total_pix)) +
# geom_area(aes(fill = land_cover, colour = land_cover), position = "fill")+
# theme_bw()+
# #scale_fill_manual("Land Cover", values = c("sienna4", "gray20", "forestgreen", "orange"), labels = c("Agriculture", "Developed", "Forest", "Grass/Shrub")) +
# #scale_colour_manual("Land Cover", values = c("sienna4", "gray20", "forestgreen", "orange"), labels = c("Agriculture", "Developed", "Forest", "Grass/Shrub")) +
# theme(legend.position = "bottom", legend.background = element_rect(colour = "grey"))Exploring the neonics
head(neonics_buffers)## YEAR RTENO buffer COMPOUND high_kg_buff low_kg_buff ag_pix_buff
## 1 1992 66085 1000 ATRAZINE 859.34 847.89 52707
## 2 1992 66085 1000 GLYPHOSATE 108.95 77.76 52707
## 3 1992 66085 10000 ATRAZINE 8348.23 8223.01 502193
## 4 1992 66085 10000 GLYPHOSATE 1075.86 795.61 502193
## 5 1992 66085 200 ATRAZINE 181.23 178.84 11132
## 6 1992 66085 200 GLYPHOSATE 22.95 16.33 11132head(neonics_buffers[is.na(neonics_buffers$high_kg_buff) | is.na(neonics_buffers$ag_pix_buff) | is.na(neonics_buffers$low_kg_buff), ])## YEAR RTENO buffer COMPOUND high_kg_buff low_kg_buff ag_pix_buff
## 141 1995 66907 10000 IMIDACLOPRID 0.4244 NA 205449
## 144 1995 66907 5000 IMIDACLOPRID 0.1192 NA 70705
## 195 1997 66085 1000 IMIDACLOPRID 1.7907 NA 52402
## 198 1997 66085 10000 IMIDACLOPRID 16.5500 NA 499972
## 201 1997 66085 200 IMIDACLOPRID 0.3804 NA 11106
## 204 1997 66085 2000 IMIDACLOPRID 3.2648 NA 95762head(neonics_buffers[is.na(neonics_buffers$high_kg_buff) | is.na(neonics_buffers$ag_pix_buff) , ])## YEAR RTENO buffer COMPOUND high_kg_buff low_kg_buff ag_pix_buff
## 2135 1992 66011 200 ATRAZINE NA NA NA
## 2136 1992 66011 200 GLYPHOSATE NA NA NA
## 2149 1992 66908 200 ATRAZINE NA NA NA
## 2150 1992 66908 200 GLYPHOSATE NA NA NA
## 2167 1993 66011 200 ATRAZINE NA NA NA
## 2168 1993 66011 200 GLYPHOSATE NA NA NAdim(neonics_buffers[is.na(neonics_buffers$high_kg_buff) | is.na(neonics_buffers$ag_pix_buff) | is.na(neonics_buffers$low_kg_buff), ])## [1] 11997 7dim(neonics_buffers[is.na(neonics_buffers$high_kg_buff) | is.na(neonics_buffers$ag_pix_buff), ])## [1] 1501 7# unique(neonics_buffers$YEAR)
# class(neonics_buffers$high_kg_buff)
# noyear_neonics_buffers <- neonics_buffers %>%
# group_by(BBS_route, buffer) %>%
# summarise(high_pest = sum(high_kg_buff), agpix = sum(ag_pix_buff))
# head(noyear_neonics_buffers)
# # spps <- spps[!is.na(spps$NMDS1) & !is.na(spps$NMDS2),]
# noyear_neonics_buffers <- noyear_neonics_buffers[!is.na(noyear_neonics_buffers$high_pest) & !is.na(noyear_neonics_buffers$agpix), ]
#
# ggplot(noyear_neonics_buffers, aes(y = as.numeric(high_pest/agpix), x = as.numeric(buffer))) +
# geom_line(aes(group = BBS_route)) +
# geom_smooth()+
# theme_bw()
#
# ggplot(noyear_neonics_buffers, aes(y = high_pest, x = buffer)) +
# geom_line(aes(group = BBS_route)) +
# geom_smooth()+
# theme_bw()
#
# ggplot(noyear_neonics_buffers, aes(y = agpix, x = buffer)) +
# geom_line(aes(group = BBS_route)) +
# geom_smooth()+
# theme_bw()
#
#
# # now to see if intensity has increased over tine
# names(neonics_buffers)
# year_neonics_buffers <- neonics_buffers %>%
# group_by(BBS_route, YEAR, COMPOUND, buffer) %>%
# summarise(intensity = high_kg_buff/ag_pix_buff)
#
# names(year_neonics_buffers)
#
# ggplot(year_neonics_buffers, aes(y = intensity, x = YEAR)) +
# geom_line(aes(group = BBS_route)) +
# facet_grid(buffer~ COMPOUND, scale = "free") +
# theme_bw()
#
# year_neonics_buffers2 <- neonics_buffers %>%
# group_by(BBS_route, YEAR, COMPOUND) %>%
# summarise(intensity = mean(high_kg_buff/ag_pix_buff))
#
# ggplot(year_neonics_buffers2, aes(y = intensity, x = YEAR)) +
# geom_line(aes(group = BBS_route)) +
# facet_wrap(~ COMPOUND, scale = "free") +
# theme_bw()
#
# ggplot(year_neonics_buffers2, aes(y = intensity, x = YEAR)) +
# geom_line(aes(group = BBS_route)) +
# facet_wrap(~ COMPOUND) +
# theme_bw()Using the big dataset: BBS_ln
head(BBS_ln)## YEAR RTENO buffer AOU ROUTEDATAID COUNTRYNUM STATENUM ROUTE RPID
## 1 1997 66001 1000 1320 6228264 840 66 1 101
## 2 1997 66001 1000 1320 6228264 840 66 1 101
## 3 1997 66001 1000 1320 6228264 840 66 1 101
## 4 1997 66001 10000 1320 6228264 840 66 1 101
## 5 1997 66001 10000 1320 6228264 840 66 1 101
## 6 1997 66001 10000 1320 6228264 840 66 1 101
## SUM_ROUTE_ABUNDANCE SCIENTIFIC_NAME X4_LETTER_CODE X6_LETTER_CODE
## 1 2 ANAS PLATYRHYNCHOS MALL ANAPLA
## 2 2 ANAS PLATYRHYNCHOS MALL ANAPLA
## 3 2 ANAS PLATYRHYNCHOS MALL ANAPLA
## 4 2 ANAS PLATYRHYNCHOS MALL ANAPLA
## 5 2 ANAS PLATYRHYNCHOS MALL ANAPLA
## 6 2 ANAS PLATYRHYNCHOS MALL ANAPLA
## HABITAT DIET NESTING BEHAVIOR CONSERVATION COMMON_NAME ORDER
## 1 lake_pond seeds ground dabbler LC Mallard Anseriformes
## 2 lake_pond seeds ground dabbler LC Mallard Anseriformes
## 3 lake_pond seeds ground dabbler LC Mallard Anseriformes
## 4 lake_pond seeds ground dabbler LC Mallard Anseriformes
## 5 lake_pond seeds ground dabbler LC Mallard Anseriformes
## 6 lake_pond seeds ground dabbler LC Mallard Anseriformes
## FAMILY GENUS SPECIES COMPOUND high_kg_buff low_kg_buff
## 1 Anatidae Anas platyrhynchos ATRAZINE NA NA
## 2 Anatidae Anas platyrhynchos GLYPHOSATE NA NA
## 3 Anatidae Anas platyrhynchos IMIDACLOPRID NA NA
## 4 Anatidae Anas platyrhynchos ATRAZINE NA NA
## 5 Anatidae Anas platyrhynchos GLYPHOSATE NA NA
## 6 Anatidae Anas platyrhynchos IMIDACLOPRID NA NA
## ag_pix_buff BBS_route agriculture barren developed forest
## 1 NA 2311 75241 0 7387 0
## 2 NA 2311 75241 0 7387 0
## 3 NA 2311 75241 0 7387 0
## 4 NA 2311 997407 1057 96264 24625
## 5 NA 2311 997407 1057 96264 24625
## 6 NA 2311 997407 1057 96264 24625
## grassland_shrub water wetlands agriculture_scaled high_kg_buff_scaled
## 1 0 13.44 0 -0.3525 NA
## 2 0 13.44 0 -0.3525 NA
## 3 0 13.44 0 -0.3525 NA
## 4 8417 8353.56 1241 3.5449 NA
## 5 8417 8353.56 1241 3.5449 NA
## 6 8417 8353.56 1241 3.5449 NAnames(BBS_ln) <- toupper(names(BBS_ln))
sort(unique(BBS_ln$DIET))## [1] birds carrion fish fruit
## [6] insects mammals nectar omnivore plants
## [11] seeds small_animals
## 12 Levels: birds carrion fish fruit insects mammals nectar ... small_animalsBBS_ln$DIET <- relevel(BBS_ln$DIET, ref = "insects")
BBS_ln$YEAR_SCALED <- scale(BBS_ln$YEAR)
BBS_ln$FOREST_SCALED <- scale(BBS_ln$FOREST)
BBS_ln$AGRICULTURE_SCALED <- scale(BBS_ln$AGRICULTURE)
BBS_ln$FOREST_PROP <- BBS_ln$FOREST/BBS_ln$AGRICULTURE
BBS_ln$HIGH_KG_BUFF <- scale(BBS_ln$HIGH_KG_BUFF)
BBS_ln$DIET_VULN <- ifelse(BBS_ln$DIET %in% c("insects", "seeds", 'nectar', "birds"), "diet_vuln", "diet_invuln")
BBS_ln$DIET_VULN <- factor(BBS_ln$DIET_VULN)
BBS_ln$DIET_INSECTS <- ifelse(BBS_ln$DIET == "insects", "diet_insects", "diet_not_insects")
BBS_ln$DIET_INSECTS <- factor(BBS_ln$DIET_INSECTS)
with(BBS_ln, cor(FOREST, AGRICULTURE))## [1] 0.262# test_mod <- glm(SUM_ROUTE_ABUNDANCE ~ YEAR_SCALED +
# AGRICULTURE_SCALED:HIGH_KG_BUFF_SCALED +
# AGRICULTURE_SCALED +
# DIET_VULN:YEAR_SCALED +
# DIET_VULN:HIGH_KG_BUFF_SCALED:YEAR_SCALED:AGRICULTURE_SCALED +
# HIGH_KG_BUFF_SCALED:COMPOUND +
# HIGH_KG_BUFF_SCALED,
# data = BBS_ln[BBS_ln$COMPOUND %in% c("ATRAZINE", "GLYPHOSATE"),],
# family = "poisson")
# summary(test_mod)
# #coefplot2(test_mod)
# #require(effects)
#
#
# test_mod2 <- glm(SUM_ROUTE_ABUNDANCE ~ YEAR_SCALED +
# AGRICULTURE_SCALED:HIGH_KG_BUFF_SCALED +
# AGRICULTURE_SCALED +
# DIET_INSECTS:YEAR_SCALED +
# DIET_INSECTS:HIGH_KG_BUFF_SCALED:YEAR_SCALED:AGRICULTURE_SCALED +
# HIGH_KG_BUFF_SCALED:COMPOUND +
# HIGH_KG_BUFF_SCALED,
# data = BBS_ln[BBS_ln$COMPOUND %in% c("ATRAZINE", "GLYPHOSATE"),],
# family = "poisson")
# summary(test_mod2)
# #coefplot2(test_mod2)
#
#
# glmer_mod_3 <- glmer(SUM_ROUTE_ABUNDANCE ~ YEAR_SCALED +
# AGRICULTURE_SCALED:HIGH_KG_BUFF_SCALED +
# AGRICULTURE_SCALED +
# DIET_INSECTS:YEAR_SCALED +
# DIET_INSECTS:HIGH_KG_BUFF_SCALED:YEAR_SCALED:AGRICULTURE_SCALED +
# HIGH_KG_BUFF_SCALED:COMPOUND +
# HIGH_KG_BUFF_SCALED +
# (1|ROUTE),
# data = BBS_ln[BBS_ln$COMPOUND %in% c("ATRAZINE", "GLYPHOSATE"),],
# family = "poisson")
# #coefplot2(glmer_mod_3)
# #coefplot2(list(glmer_mod_3, test_mod2))
#
# glmer_mod_4 <- glmer(SUM_ROUTE_ABUNDANCE ~ YEAR_SCALED +
# AGRICULTURE_SCALED:HIGH_KG_BUFF_SCALED +
# AGRICULTURE_SCALED +
# DIET_INSECTS:YEAR_SCALED +
# DIET_INSECTS +
# DIET_INSECTS:HIGH_KG_BUFF_SCALED:YEAR_SCALED:AGRICULTURE_SCALED +
# HIGH_KG_BUFF_SCALED:COMPOUND +
# HIGH_KG_BUFF_SCALED +
# (1|ROUTE),
# data = BBS_ln[BBS_ln$COMPOUND %in% c("ATRAZINE", "GLYPHOSATE"),],
# family = "poisson")
# summary(glmer_mod_4)
# #coefplot2(glmer_mod_4)
# #coefplot2(list(glmer_mod_3, test_mod2, glmer_mod_4))
#
# glmer_mod_5 <- glmer(SUM_ROUTE_ABUNDANCE ~
# AGRICULTURE_SCALED:DIET_INSECTS +
# AGRICULTURE_SCALED:HIGH_KG_BUFF_SCALED +
# FOREST_SCALED:DIET_INSECTS +
# DIET_INSECTS:HIGH_KG_BUFF_SCALED:AGRICULTURE_SCALED +
# HIGH_KG_BUFF_SCALED:COMPOUND:DIET_INSECTS +
# HIGH_KG_BUFF_SCALED:DIET_INSECTS +
# (ROUTE|YEAR),
# data = BBS_ln[BBS_ln$COMPOUND %in% c("ATRAZINE", "GLYPHOSATE"),],
# family = "poisson")
# summary(glmer_mod_5)
#coefplot2(glmer_mod_5)
#coefplot2(list(glmer_mod_3, test_mod2, glmer_mod_4, glmer_mod_5))Aggregating based on diet and then modelling
head(BBS_ln)## YEAR RTENO BUFFER AOU ROUTEDATAID COUNTRYNUM STATENUM ROUTE RPID
## 1 1997 66001 1000 1320 6228264 840 66 1 101
## 2 1997 66001 1000 1320 6228264 840 66 1 101
## 3 1997 66001 1000 1320 6228264 840 66 1 101
## 4 1997 66001 10000 1320 6228264 840 66 1 101
## 5 1997 66001 10000 1320 6228264 840 66 1 101
## 6 1997 66001 10000 1320 6228264 840 66 1 101
## SUM_ROUTE_ABUNDANCE SCIENTIFIC_NAME X4_LETTER_CODE X6_LETTER_CODE
## 1 2 ANAS PLATYRHYNCHOS MALL ANAPLA
## 2 2 ANAS PLATYRHYNCHOS MALL ANAPLA
## 3 2 ANAS PLATYRHYNCHOS MALL ANAPLA
## 4 2 ANAS PLATYRHYNCHOS MALL ANAPLA
## 5 2 ANAS PLATYRHYNCHOS MALL ANAPLA
## 6 2 ANAS PLATYRHYNCHOS MALL ANAPLA
## HABITAT DIET NESTING BEHAVIOR CONSERVATION COMMON_NAME ORDER
## 1 lake_pond seeds ground dabbler LC Mallard Anseriformes
## 2 lake_pond seeds ground dabbler LC Mallard Anseriformes
## 3 lake_pond seeds ground dabbler LC Mallard Anseriformes
## 4 lake_pond seeds ground dabbler LC Mallard Anseriformes
## 5 lake_pond seeds ground dabbler LC Mallard Anseriformes
## 6 lake_pond seeds ground dabbler LC Mallard Anseriformes
## FAMILY GENUS SPECIES COMPOUND HIGH_KG_BUFF LOW_KG_BUFF
## 1 Anatidae Anas platyrhynchos ATRAZINE NA NA
## 2 Anatidae Anas platyrhynchos GLYPHOSATE NA NA
## 3 Anatidae Anas platyrhynchos IMIDACLOPRID NA NA
## 4 Anatidae Anas platyrhynchos ATRAZINE NA NA
## 5 Anatidae Anas platyrhynchos GLYPHOSATE NA NA
## 6 Anatidae Anas platyrhynchos IMIDACLOPRID NA NA
## AG_PIX_BUFF BBS_ROUTE AGRICULTURE BARREN DEVELOPED FOREST
## 1 NA 2311 75241 0 7387 0
## 2 NA 2311 75241 0 7387 0
## 3 NA 2311 75241 0 7387 0
## 4 NA 2311 997407 1057 96264 24625
## 5 NA 2311 997407 1057 96264 24625
## 6 NA 2311 997407 1057 96264 24625
## GRASSLAND_SHRUB WATER WETLANDS AGRICULTURE_SCALED HIGH_KG_BUFF_SCALED
## 1 0 13.44 0 -0.3525 NA
## 2 0 13.44 0 -0.3525 NA
## 3 0 13.44 0 -0.3525 NA
## 4 8417 8353.56 1241 3.5449 NA
## 5 8417 8353.56 1241 3.5449 NA
## 6 8417 8353.56 1241 3.5449 NA
## YEAR_SCALED FOREST_SCALED FOREST_PROP DIET_VULN DIET_INSECTS
## 1 -1.882 -0.6396 0.00000 diet_vuln diet_not_insects
## 2 -1.882 -0.6396 0.00000 diet_vuln diet_not_insects
## 3 -1.882 -0.6396 0.00000 diet_vuln diet_not_insects
## 4 -1.882 -0.5070 0.02469 diet_vuln diet_not_insects
## 5 -1.882 -0.5070 0.02469 diet_vuln diet_not_insects
## 6 -1.882 -0.5070 0.02469 diet_vuln diet_not_insectsnames(BBS_ln)## [1] "YEAR" "RTENO" "BUFFER"
## [4] "AOU" "ROUTEDATAID" "COUNTRYNUM"
## [7] "STATENUM" "ROUTE" "RPID"
## [10] "SUM_ROUTE_ABUNDANCE" "SCIENTIFIC_NAME" "X4_LETTER_CODE"
## [13] "X6_LETTER_CODE" "HABITAT" "DIET"
## [16] "NESTING" "BEHAVIOR" "CONSERVATION"
## [19] "COMMON_NAME" "ORDER" "FAMILY"
## [22] "GENUS" "SPECIES" "COMPOUND"
## [25] "HIGH_KG_BUFF" "LOW_KG_BUFF" "AG_PIX_BUFF"
## [28] "BBS_ROUTE" "AGRICULTURE" "BARREN"
## [31] "DEVELOPED" "FOREST" "GRASSLAND_SHRUB"
## [34] "WATER" "WETLANDS" "AGRICULTURE_SCALED"
## [37] "HIGH_KG_BUFF_SCALED" "YEAR_SCALED" "FOREST_SCALED"
## [40] "FOREST_PROP" "DIET_VULN" "DIET_INSECTS"BBS_ln$HIGH_KG_BUFF <- as.numeric(BBS_ln$HIGH_KG_BUFF)
BBS_ln$AGRICULTURE_SCALED <- as.numeric(BBS_ln$AGRICULTURE_SCALED)
BBS_ln$HIGH_KG_BUFF_SCALED <- as.numeric(BBS_ln$HIGH_KG_BUFF_SCALED)
BBS_ln$YEAR_SCALED <- as.numeric(BBS_ln$YEAR_SCALED)
BBS_ln$FOREST_SCALED <- as.numeric(BBS_ln$FOREST_SCALED)
BBS_ln_diet <- data.frame(BBS_ln %>% group_by(YEAR, RTENO, BBS_ROUTE, BUFFER, ROUTE, COMPOUND, HIGH_KG_BUFF, AGRICULTURE, FOREST, DEVELOPED, AGRICULTURE_SCALED, YEAR_SCALED, FOREST_SCALED, HIGH_KG_BUFF_SCALED, FOREST_PROP, DIET) %>%
summarise(SUM_ROUTE_ABUNDANCE_DIET = sum(SUM_ROUTE_ABUNDANCE)))
head(BBS_ln_diet)## YEAR RTENO BBS_ROUTE BUFFER ROUTE COMPOUND HIGH_KG_BUFF AGRICULTURE
## 1 1995 66061 2342 200 61 ATRAZINE -0.2387 12628
## 2 1995 66061 2342 200 61 ATRAZINE -0.2387 12628
## 3 1995 66061 2342 200 61 ATRAZINE -0.2387 12628
## 4 1995 66061 2342 200 61 ATRAZINE -0.2387 12628
## 5 1995 66061 2342 200 61 ATRAZINE -0.2387 12628
## 6 1995 66061 2342 200 61 ATRAZINE -0.2387 12628
## FOREST DEVELOPED AGRICULTURE_SCALED YEAR_SCALED FOREST_SCALED
## 1 2330 1241 -0.6171 -2.37 -0.6271
## 2 2330 1241 -0.6171 -2.37 -0.6271
## 3 2330 1241 -0.6171 -2.37 -0.6271
## 4 2330 1241 -0.6171 -2.37 -0.6271
## 5 2330 1241 -0.6171 -2.37 -0.6271
## 6 2330 1241 -0.6171 -2.37 -0.6271
## HIGH_KG_BUFF_SCALED FOREST_PROP DIET SUM_ROUTE_ABUNDANCE_DIET
## 1 -0.2387 0.1845 insects 1460
## 2 -0.2387 0.1845 carrion 3
## 3 -0.2387 0.1845 fish 3
## 4 -0.2387 0.1845 fruit 40
## 5 -0.2387 0.1845 nectar 1
## 6 -0.2387 0.1845 omnivore 312class(BBS_ln_diet$ROUTE)## [1] "integer"BBS_ln_diet$YEAR_fac <- as.factor(BBS_ln_diet$YEAR)
BBS_ln_diet$ROUTE <- as.factor(BBS_ln_diet$ROUTE)
range(na.omit(BBS_ln_diet$HIGH_KG_BUFF_SCALED))## [1] -0.2858 14.3051range(na.omit(BBS_ln_diet$AGRICULTURE_SCALED))## [1] -0.6705 4.4323range(na.omit(BBS_ln_diet$FOREST_SCALED))## [1] -0.6396 4.7383range(na.omit(BBS_ln_diet$FOREST_SCALED))## [1] -0.6396 4.7383class(BBS_ln_diet$DIET)## [1] "factor"class(BBS_ln_diet$ROUTE)## [1] "factor"range(BBS_ln_diet$SUM_ROUTE_ABUNDANCE_DIET)## [1] 1 1500ggplot(BBS_ln_diet, aes(x = ROUTE, y = SUM_ROUTE_ABUNDANCE_DIET)) +
geom_point() +
geom_boxplot() + theme_bw() +
facet_wrap(~ YEAR)
BBS_ln_diet$sqrt_response <- round(sqrt(BBS_ln_diet$SUM_ROUTE_ABUNDANCE_DIET), 0)
range(BBS_ln_diet$sqrt_response)## [1] 1 39BBS_ln_dietcc <- BBS_ln_diet[complete.cases(BBS_ln_diet),]
str(BBS_ln_dietcc)## 'data.frame': 178487 obs. of 19 variables:
## $ YEAR : int 1995 1995 1995 1995 1995 1995 1995 1995 1995 1995 ...
## $ RTENO : int 66061 66061 66061 66061 66061 66061 66061 66061 66061 66061 ...
## $ BBS_ROUTE : int 2342 2342 2342 2342 2342 2342 2342 2342 2342 2342 ...
## $ BUFFER : int 200 200 200 200 200 200 200 200 200 200 ...
## $ ROUTE : Factor w/ 66 levels "1","2","3","5",..: 30 30 30 30 30 30 30 30 30 30 ...
## $ COMPOUND : Factor w/ 8 levels "ACETAMIPRID",..: 2 2 2 2 2 2 2 2 5 5 ...
## $ HIGH_KG_BUFF : num -0.239 -0.239 -0.239 -0.239 -0.239 ...
## $ AGRICULTURE : num 12628 12628 12628 12628 12628 ...
## $ FOREST : num 2330 2330 2330 2330 2330 ...
## $ DEVELOPED : num 1241 1241 1241 1241 1241 ...
## $ AGRICULTURE_SCALED : num -0.617 -0.617 -0.617 -0.617 -0.617 ...
## $ YEAR_SCALED : num -2.37 -2.37 -2.37 -2.37 -2.37 ...
## $ FOREST_SCALED : num -0.627 -0.627 -0.627 -0.627 -0.627 ...
## $ HIGH_KG_BUFF_SCALED : num -0.239 -0.239 -0.239 -0.239 -0.239 ...
## $ FOREST_PROP : num 0.184 0.184 0.184 0.184 0.184 ...
## $ DIET : Factor w/ 12 levels "insects","","birds",..: 1 4 5 6 8 9 10 11 1 4 ...
## $ SUM_ROUTE_ABUNDANCE_DIET: num 1460 3 3 40 1 312 22 337 1460 3 ...
## $ YEAR_fac : Factor w/ 17 levels "1995","1996",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ sqrt_response : num 38 2 2 6 1 18 5 18 38 2 ...load("predicting_glmers.Rdata")
# glmer_mod_6 <- glmer(na.action = na.omit,
# sqrt_response ~
# AGRICULTURE_SCALED:DIET+
# AGRICULTURE_SCALED:HIGH_KG_BUFF_SCALED +
# FOREST_SCALED:DIET +
# DIET:HIGH_KG_BUFF_SCALED:AGRICULTURE_SCALED +
# HIGH_KG_BUFF_SCALED:COMPOUND:DIET +
# HIGH_KG_BUFF_SCALED:DIET +
# (1|YEAR_fac) + (1|ROUTE),
# data = BBS_ln_dietcc[BBS_ln_dietcc$COMPOUND %in% c("ATRAZINE",
# "GLYPHOSATE")
# & BBS_ln_dietcc$YEAR > 1996
# & BBS_ln_dietcc$DIET != "",],
# family = "poisson",
# verbose = TRUE)
# sum_glmer_mod_6<-summary(glmer_mod_6)
# cf2_glmer_mod_6 <- coefplot2(glmer_mod_6)
# save(glmer_mod_6, sum_glmer_mod_6, cf2_glmer_mod_6, file = "glmer_mod_6.Rdata")
#load( "glmer_mod_6.Rdata")
#save.image("predicting_glmers.Rdata")
diet_levels <- levels(BBS_ln_dietcc$DIET)[-2]
ep <- expand.grid(AGRICULTURE_SCALED = seq(from = min(BBS_ln_dietcc$AGRICULTURE_SCALED),
to = max(BBS_ln_dietcc$AGRICULTURE_SCALED),
by = 0.4),
FOREST_SCALED = mean(BBS_ln_dietcc$FOREST_SCALED),
DIET = diet_levels,
HIGH_KG_BUFF_SCALED =seq(from = min(BBS_ln_dietcc$HIGH_KG_BUFF_SCALED),
to = max(BBS_ln_dietcc$HIGH_KG_BUFF_SCALED),
by = 2),
COMPOUND = c("GLYPHOSATE", "ATRAZINE")
)
mm <- model.matrix(delete.response(terms(glmer_mod_6)), ep)
ep <- data.frame(ep, response = mm %*% fixef(glmer_mod_6))
ep <- with(ep, data.frame(ep, transformed.response = exp(response)))
pvar1 <- diag(mm %*% tcrossprod(vcov(glmer_mod_6), mm))
tvar1 <- pvar1 + VarCorr(glmer_mod_6)$YEAR + VarCorr(glmer_mod_6)$ROUTE
ep <- data.frame(ep, pse=sqrt(pvar1), tse=sqrt(tvar1))
ep <- with(ep,
data.frame(ep,
plo=exp(response-1.96*pse),
phi=exp(response+1.96*pse),
tlo=exp(response-1.96*tse),
thi=exp(response+1.96*tse)))
head(ep)## AGRICULTURE_SCALED FOREST_SCALED DIET HIGH_KG_BUFF_SCALED COMPOUND
## 1 -0.6721 -0.004414 insects -0.2818 GLYPHOSATE
## 2 -0.2721 -0.004414 insects -0.2818 GLYPHOSATE
## 3 0.1279 -0.004414 insects -0.2818 GLYPHOSATE
## 4 0.5279 -0.004414 insects -0.2818 GLYPHOSATE
## 5 0.9279 -0.004414 insects -0.2818 GLYPHOSATE
## 6 1.3279 -0.004414 insects -0.2818 GLYPHOSATE
## response transformed.response pse tse plo phi tlo thi
## 1 2.679 14.577 0.02996 0.1735 13.746 15.458 10.374 20.483
## 2 2.478 11.923 0.02981 0.1735 11.247 12.640 8.486 16.753
## 3 2.278 9.753 0.02981 0.1735 9.199 10.339 6.941 13.703
## 4 2.077 7.977 0.02998 0.1735 7.522 8.460 5.677 11.209
## 5 1.876 6.525 0.03030 0.1736 6.149 6.924 4.643 9.170
## 6 1.675 5.337 0.03077 0.1737 5.025 5.669 3.797 7.502names(ep) <- tolower(names(ep))
names(ep) ## [1] "agriculture_scaled" "forest_scaled" "diet"
## [4] "high_kg_buff_scaled" "compound" "response"
## [7] "transformed.response" "pse" "tse"
## [10] "plo" "phi" "tlo"
## [13] "thi"ep1 <- ep[ep$diet %in% c("insects", "omnivore"), ]
require(akima)## Loading required package: akimaep2 <- with(ep1[ep1$compound == "GLYPHOSATE" & ep1$diet == "insects",], interp(agriculture_scaled, high_kg_buff_scaled, transformed.response))
ep3 <- with(ep1[ep1$compound == "GLYPHOSATE" & ep1$diet == "omnivore",], interp(agriculture_scaled, high_kg_buff_scaled, transformed.response))
ep4 <- with(ep1[ep1$compound == "ATRAZINE" & ep1$diet == "insects",], interp(agriculture_scaled, high_kg_buff_scaled, transformed.response))
ep5 <- with(ep1[ep1$compound == "ATRAZINE" & ep1$diet == "omnivore",], interp(agriculture_scaled, high_kg_buff_scaled, transformed.response))
with(ep2, persp(x, y, z, theta = 330, phi = 20,
xlab = "Agriculture",
ylab = "Pesticide application rate",
zlab = "Bird response",
col = "royalblue",
#nticks = 5,
#ticktype = "detailed",
shade = 0.75))
with(ep3, persp(x, y, z, theta = 55, phi = 30,
xlab = "Agriculture",
ylab = "Pesticide application rate",
zlab = "Bird response",
col = "cornflowerblue",
#nticks = 5,
#ticktype = "detailed",
shade = 0.75))
with(ep4, persp(x, y, z, theta = 280, phi = 30,
xlab = "Agriculture",
ylab = "Pesticide application rate",
zlab = "Bird response",
col = "orange",
#nticks = 5,
#ticktype = "detailed",
shade = 0.75))
with(ep5, persp(x, y, z, theta = 55, phi = 30,
xlab = "Agriculture",
ylab = "Pesticide application rate",
zlab = "Bird response",
col = "violetred",
#nticks = 5,
#ticktype = "detailed",
shade = 0.2))
Earlier glm model which you can run if interested, heavily pseudo-replicated however.
glm_mod_6 <- glm(na.action = na.omit,
sqrt_response ~
DIET+
HIGH_KG_BUFF_SCALED +
AGRICULTURE_SCALED:DIET+
AGRICULTURE_SCALED:HIGH_KG_BUFF_SCALED +
DIET:HIGH_KG_BUFF_SCALED:AGRICULTURE_SCALED +
HIGH_KG_BUFF_SCALED:COMPOUND:DIET +
HIGH_KG_BUFF_SCALED:DIET,
data = BBS_ln_dietcc[BBS_ln_dietcc$COMPOUND %in% c("ATRAZINE",
"GLYPHOSATE")
& BBS_ln_dietcc$YEAR > 1996
& BBS_ln_dietcc$DIET != "",],
family = "poisson")
summary(glm_mod_6)##
## Call:
## glm(formula = sqrt_response ~ DIET + HIGH_KG_BUFF_SCALED + AGRICULTURE_SCALED:DIET +
## AGRICULTURE_SCALED:HIGH_KG_BUFF_SCALED + DIET:HIGH_KG_BUFF_SCALED:AGRICULTURE_SCALED +
## HIGH_KG_BUFF_SCALED:COMPOUND:DIET + HIGH_KG_BUFF_SCALED:DIET,
## family = "poisson", data = BBS_ln_dietcc[BBS_ln_dietcc$COMPOUND %in%
## c("ATRAZINE", "GLYPHOSATE") & BBS_ln_dietcc$YEAR > 1996 &
## BBS_ln_dietcc$DIET != "", ], na.action = na.omit)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -3.124 -0.518 -0.143 0.452 4.097
##
## Coefficients:
## Estimate
## (Intercept) 3.175148
## DIETbirds -3.134051
## DIETcarrion -2.230065
## DIETfish -2.686719
## DIETfruit -1.767611
## DIETmammals -2.946772
## DIETnectar -2.966412
## DIETomnivore -0.731661
## DIETplants -2.436557
## DIETseeds -0.547610
## DIETsmall_animals -2.846654
## HIGH_KG_BUFF_SCALED -0.027365
## DIETinsects:AGRICULTURE_SCALED 0.029820
## DIETbirds:AGRICULTURE_SCALED 0.005821
## DIETcarrion:AGRICULTURE_SCALED 0.084879
## DIETfish:AGRICULTURE_SCALED 0.020911
## DIETfruit:AGRICULTURE_SCALED 0.029860
## DIETmammals:AGRICULTURE_SCALED 0.039498
## DIETnectar:AGRICULTURE_SCALED -0.017187
## DIETomnivore:AGRICULTURE_SCALED 0.018079
## DIETplants:AGRICULTURE_SCALED -0.088285
## DIETseeds:AGRICULTURE_SCALED 0.026454
## DIETsmall_animals:AGRICULTURE_SCALED 0.016368
## HIGH_KG_BUFF_SCALED:AGRICULTURE_SCALED 0.001428
## DIETbirds:HIGH_KG_BUFF_SCALED 0.016325
## DIETcarrion:HIGH_KG_BUFF_SCALED -0.107665
## DIETfish:HIGH_KG_BUFF_SCALED 0.030891
## DIETfruit:HIGH_KG_BUFF_SCALED -0.046663
## DIETmammals:HIGH_KG_BUFF_SCALED -0.030550
## DIETnectar:HIGH_KG_BUFF_SCALED -0.013445
## DIETomnivore:HIGH_KG_BUFF_SCALED 0.006816
## DIETplants:HIGH_KG_BUFF_SCALED 0.127757
## DIETseeds:HIGH_KG_BUFF_SCALED 0.074079
## DIETsmall_animals:HIGH_KG_BUFF_SCALED 0.004861
## DIETbirds:HIGH_KG_BUFF_SCALED:AGRICULTURE_SCALED -0.000578
## DIETcarrion:HIGH_KG_BUFF_SCALED:AGRICULTURE_SCALED 0.010275
## DIETfish:HIGH_KG_BUFF_SCALED:AGRICULTURE_SCALED -0.005944
## DIETfruit:HIGH_KG_BUFF_SCALED:AGRICULTURE_SCALED 0.002099
## DIETmammals:HIGH_KG_BUFF_SCALED:AGRICULTURE_SCALED 0.005676
## DIETnectar:HIGH_KG_BUFF_SCALED:AGRICULTURE_SCALED 0.006526
## DIETomnivore:HIGH_KG_BUFF_SCALED:AGRICULTURE_SCALED -0.000291
## DIETplants:HIGH_KG_BUFF_SCALED:AGRICULTURE_SCALED 0.001589
## DIETseeds:HIGH_KG_BUFF_SCALED:AGRICULTURE_SCALED -0.013408
## DIETsmall_animals:HIGH_KG_BUFF_SCALED:AGRICULTURE_SCALED -0.002292
## DIETinsects:HIGH_KG_BUFF_SCALED:COMPOUNDGLYPHOSATE 0.009158
## DIETbirds:HIGH_KG_BUFF_SCALED:COMPOUNDGLYPHOSATE 0.003118
## DIETcarrion:HIGH_KG_BUFF_SCALED:COMPOUNDGLYPHOSATE 0.034232
## DIETfish:HIGH_KG_BUFF_SCALED:COMPOUNDGLYPHOSATE -0.000772
## DIETfruit:HIGH_KG_BUFF_SCALED:COMPOUNDGLYPHOSATE 0.021467
## DIETmammals:HIGH_KG_BUFF_SCALED:COMPOUNDGLYPHOSATE 0.007322
## DIETnectar:HIGH_KG_BUFF_SCALED:COMPOUNDGLYPHOSATE 0.002277
## DIETomnivore:HIGH_KG_BUFF_SCALED:COMPOUNDGLYPHOSATE 0.005580
## DIETplants:HIGH_KG_BUFF_SCALED:COMPOUNDGLYPHOSATE -0.035643
## DIETseeds:HIGH_KG_BUFF_SCALED:COMPOUNDGLYPHOSATE -0.005885
## DIETsmall_animals:HIGH_KG_BUFF_SCALED:COMPOUNDGLYPHOSATE 0.010567
## Std. Error
## (Intercept) 0.002811
## DIETbirds 0.029237
## DIETcarrion 0.010397
## DIETfish 0.012308
## DIETfruit 0.008262
## DIETmammals 0.025355
## DIETnectar 0.019745
## DIETomnivore 0.004942
## DIETplants 0.017813
## DIETseeds 0.004607
## DIETsmall_animals 0.014476
## HIGH_KG_BUFF_SCALED 0.005701
## DIETinsects:AGRICULTURE_SCALED 0.005173
## DIETbirds:AGRICULTURE_SCALED 0.053022
## DIETcarrion:AGRICULTURE_SCALED 0.018927
## DIETfish:AGRICULTURE_SCALED 0.021730
## DIETfruit:AGRICULTURE_SCALED 0.014734
## DIETmammals:AGRICULTURE_SCALED 0.049625
## DIETnectar:AGRICULTURE_SCALED 0.039123
## DIETomnivore:AGRICULTURE_SCALED 0.007512
## DIETplants:AGRICULTURE_SCALED 0.030605
## DIETseeds:AGRICULTURE_SCALED 0.006612
## DIETsmall_animals:AGRICULTURE_SCALED 0.026318
## HIGH_KG_BUFF_SCALED:AGRICULTURE_SCALED 0.001109
## DIETbirds:HIGH_KG_BUFF_SCALED 0.059939
## DIETcarrion:HIGH_KG_BUFF_SCALED 0.022122
## DIETfish:HIGH_KG_BUFF_SCALED 0.024384
## DIETfruit:HIGH_KG_BUFF_SCALED 0.017615
## DIETmammals:HIGH_KG_BUFF_SCALED 0.057678
## DIETnectar:HIGH_KG_BUFF_SCALED 0.045433
## DIETomnivore:HIGH_KG_BUFF_SCALED 0.010036
## DIETplants:HIGH_KG_BUFF_SCALED 0.031491
## DIETseeds:HIGH_KG_BUFF_SCALED 0.009155
## DIETsmall_animals:HIGH_KG_BUFF_SCALED 0.029825
## DIETbirds:HIGH_KG_BUFF_SCALED:AGRICULTURE_SCALED 0.011957
## DIETcarrion:HIGH_KG_BUFF_SCALED:AGRICULTURE_SCALED 0.004264
## DIETfish:HIGH_KG_BUFF_SCALED:AGRICULTURE_SCALED 0.004806
## DIETfruit:HIGH_KG_BUFF_SCALED:AGRICULTURE_SCALED 0.003469
## DIETmammals:HIGH_KG_BUFF_SCALED:AGRICULTURE_SCALED 0.011524
## DIETnectar:HIGH_KG_BUFF_SCALED:AGRICULTURE_SCALED 0.009166
## DIETomnivore:HIGH_KG_BUFF_SCALED:AGRICULTURE_SCALED 0.001951
## DIETplants:HIGH_KG_BUFF_SCALED:AGRICULTURE_SCALED 0.005777
## DIETseeds:HIGH_KG_BUFF_SCALED:AGRICULTURE_SCALED 0.001786
## DIETsmall_animals:HIGH_KG_BUFF_SCALED:AGRICULTURE_SCALED 0.005722
## DIETinsects:HIGH_KG_BUFF_SCALED:COMPOUNDGLYPHOSATE 0.003159
## DIETbirds:HIGH_KG_BUFF_SCALED:COMPOUNDGLYPHOSATE 0.032824
## DIETcarrion:HIGH_KG_BUFF_SCALED:COMPOUNDGLYPHOSATE 0.012106
## DIETfish:HIGH_KG_BUFF_SCALED:COMPOUNDGLYPHOSATE 0.013002
## DIETfruit:HIGH_KG_BUFF_SCALED:COMPOUNDGLYPHOSATE 0.009379
## DIETmammals:HIGH_KG_BUFF_SCALED:COMPOUNDGLYPHOSATE 0.033707
## DIETnectar:HIGH_KG_BUFF_SCALED:COMPOUNDGLYPHOSATE 0.025555
## DIETomnivore:HIGH_KG_BUFF_SCALED:COMPOUNDGLYPHOSATE 0.004578
## DIETplants:HIGH_KG_BUFF_SCALED:COMPOUNDGLYPHOSATE 0.014605
## DIETseeds:HIGH_KG_BUFF_SCALED:COMPOUNDGLYPHOSATE 0.003939
## DIETsmall_animals:HIGH_KG_BUFF_SCALED:COMPOUNDGLYPHOSATE 0.016377
## z value Pr(>|z|)
## (Intercept) 1129.49 < 2e-16
## DIETbirds -107.20 < 2e-16
## DIETcarrion -214.49 < 2e-16
## DIETfish -218.29 < 2e-16
## DIETfruit -213.95 < 2e-16
## DIETmammals -116.22 < 2e-16
## DIETnectar -150.24 < 2e-16
## DIETomnivore -148.06 < 2e-16
## DIETplants -136.79 < 2e-16
## DIETseeds -118.86 < 2e-16
## DIETsmall_animals -196.65 < 2e-16
## HIGH_KG_BUFF_SCALED -4.80 1.6e-06
## DIETinsects:AGRICULTURE_SCALED 5.76 8.2e-09
## DIETbirds:AGRICULTURE_SCALED 0.11 0.9126
## DIETcarrion:AGRICULTURE_SCALED 4.48 7.3e-06
## DIETfish:AGRICULTURE_SCALED 0.96 0.3359
## DIETfruit:AGRICULTURE_SCALED 2.03 0.0427
## DIETmammals:AGRICULTURE_SCALED 0.80 0.4261
## DIETnectar:AGRICULTURE_SCALED -0.44 0.6604
## DIETomnivore:AGRICULTURE_SCALED 2.41 0.0161
## DIETplants:AGRICULTURE_SCALED -2.88 0.0039
## DIETseeds:AGRICULTURE_SCALED 4.00 6.3e-05
## DIETsmall_animals:AGRICULTURE_SCALED 0.62 0.5340
## HIGH_KG_BUFF_SCALED:AGRICULTURE_SCALED 1.29 0.1978
## DIETbirds:HIGH_KG_BUFF_SCALED 0.27 0.7853
## DIETcarrion:HIGH_KG_BUFF_SCALED -4.87 1.1e-06
## DIETfish:HIGH_KG_BUFF_SCALED 1.27 0.2052
## DIETfruit:HIGH_KG_BUFF_SCALED -2.65 0.0081
## DIETmammals:HIGH_KG_BUFF_SCALED -0.53 0.5963
## DIETnectar:HIGH_KG_BUFF_SCALED -0.30 0.7673
## DIETomnivore:HIGH_KG_BUFF_SCALED 0.68 0.4970
## DIETplants:HIGH_KG_BUFF_SCALED 4.06 5.0e-05
## DIETseeds:HIGH_KG_BUFF_SCALED 8.09 5.9e-16
## DIETsmall_animals:HIGH_KG_BUFF_SCALED 0.16 0.8705
## DIETbirds:HIGH_KG_BUFF_SCALED:AGRICULTURE_SCALED -0.05 0.9614
## DIETcarrion:HIGH_KG_BUFF_SCALED:AGRICULTURE_SCALED 2.41 0.0159
## DIETfish:HIGH_KG_BUFF_SCALED:AGRICULTURE_SCALED -1.24 0.2162
## DIETfruit:HIGH_KG_BUFF_SCALED:AGRICULTURE_SCALED 0.61 0.5451
## DIETmammals:HIGH_KG_BUFF_SCALED:AGRICULTURE_SCALED 0.49 0.6223
## DIETnectar:HIGH_KG_BUFF_SCALED:AGRICULTURE_SCALED 0.71 0.4764
## DIETomnivore:HIGH_KG_BUFF_SCALED:AGRICULTURE_SCALED -0.15 0.8816
## DIETplants:HIGH_KG_BUFF_SCALED:AGRICULTURE_SCALED 0.28 0.7832
## DIETseeds:HIGH_KG_BUFF_SCALED:AGRICULTURE_SCALED -7.51 6.1e-14
## DIETsmall_animals:HIGH_KG_BUFF_SCALED:AGRICULTURE_SCALED -0.40 0.6888
## DIETinsects:HIGH_KG_BUFF_SCALED:COMPOUNDGLYPHOSATE 2.90 0.0037
## DIETbirds:HIGH_KG_BUFF_SCALED:COMPOUNDGLYPHOSATE 0.09 0.9243
## DIETcarrion:HIGH_KG_BUFF_SCALED:COMPOUNDGLYPHOSATE 2.83 0.0047
## DIETfish:HIGH_KG_BUFF_SCALED:COMPOUNDGLYPHOSATE -0.06 0.9526
## DIETfruit:HIGH_KG_BUFF_SCALED:COMPOUNDGLYPHOSATE 2.29 0.0221
## DIETmammals:HIGH_KG_BUFF_SCALED:COMPOUNDGLYPHOSATE 0.22 0.8280
## DIETnectar:HIGH_KG_BUFF_SCALED:COMPOUNDGLYPHOSATE 0.09 0.9290
## DIETomnivore:HIGH_KG_BUFF_SCALED:COMPOUNDGLYPHOSATE 1.22 0.2229
## DIETplants:HIGH_KG_BUFF_SCALED:COMPOUNDGLYPHOSATE -2.44 0.0147
## DIETseeds:HIGH_KG_BUFF_SCALED:COMPOUNDGLYPHOSATE -1.49 0.1352
## DIETsmall_animals:HIGH_KG_BUFF_SCALED:COMPOUNDGLYPHOSATE 0.65 0.5188
##
## (Intercept) ***
## DIETbirds ***
## DIETcarrion ***
## DIETfish ***
## DIETfruit ***
## DIETmammals ***
## DIETnectar ***
## DIETomnivore ***
## DIETplants ***
## DIETseeds ***
## DIETsmall_animals ***
## HIGH_KG_BUFF_SCALED ***
## DIETinsects:AGRICULTURE_SCALED ***
## DIETbirds:AGRICULTURE_SCALED
## DIETcarrion:AGRICULTURE_SCALED ***
## DIETfish:AGRICULTURE_SCALED
## DIETfruit:AGRICULTURE_SCALED *
## DIETmammals:AGRICULTURE_SCALED
## DIETnectar:AGRICULTURE_SCALED
## DIETomnivore:AGRICULTURE_SCALED *
## DIETplants:AGRICULTURE_SCALED **
## DIETseeds:AGRICULTURE_SCALED ***
## DIETsmall_animals:AGRICULTURE_SCALED
## HIGH_KG_BUFF_SCALED:AGRICULTURE_SCALED
## DIETbirds:HIGH_KG_BUFF_SCALED
## DIETcarrion:HIGH_KG_BUFF_SCALED ***
## DIETfish:HIGH_KG_BUFF_SCALED
## DIETfruit:HIGH_KG_BUFF_SCALED **
## DIETmammals:HIGH_KG_BUFF_SCALED
## DIETnectar:HIGH_KG_BUFF_SCALED
## DIETomnivore:HIGH_KG_BUFF_SCALED
## DIETplants:HIGH_KG_BUFF_SCALED ***
## DIETseeds:HIGH_KG_BUFF_SCALED ***
## DIETsmall_animals:HIGH_KG_BUFF_SCALED
## DIETbirds:HIGH_KG_BUFF_SCALED:AGRICULTURE_SCALED
## DIETcarrion:HIGH_KG_BUFF_SCALED:AGRICULTURE_SCALED *
## DIETfish:HIGH_KG_BUFF_SCALED:AGRICULTURE_SCALED
## DIETfruit:HIGH_KG_BUFF_SCALED:AGRICULTURE_SCALED
## DIETmammals:HIGH_KG_BUFF_SCALED:AGRICULTURE_SCALED
## DIETnectar:HIGH_KG_BUFF_SCALED:AGRICULTURE_SCALED
## DIETomnivore:HIGH_KG_BUFF_SCALED:AGRICULTURE_SCALED
## DIETplants:HIGH_KG_BUFF_SCALED:AGRICULTURE_SCALED
## DIETseeds:HIGH_KG_BUFF_SCALED:AGRICULTURE_SCALED ***
## DIETsmall_animals:HIGH_KG_BUFF_SCALED:AGRICULTURE_SCALED
## DIETinsects:HIGH_KG_BUFF_SCALED:COMPOUNDGLYPHOSATE **
## DIETbirds:HIGH_KG_BUFF_SCALED:COMPOUNDGLYPHOSATE
## DIETcarrion:HIGH_KG_BUFF_SCALED:COMPOUNDGLYPHOSATE **
## DIETfish:HIGH_KG_BUFF_SCALED:COMPOUNDGLYPHOSATE
## DIETfruit:HIGH_KG_BUFF_SCALED:COMPOUNDGLYPHOSATE *
## DIETmammals:HIGH_KG_BUFF_SCALED:COMPOUNDGLYPHOSATE
## DIETnectar:HIGH_KG_BUFF_SCALED:COMPOUNDGLYPHOSATE
## DIETomnivore:HIGH_KG_BUFF_SCALED:COMPOUNDGLYPHOSATE
## DIETplants:HIGH_KG_BUFF_SCALED:COMPOUNDGLYPHOSATE *
## DIETseeds:HIGH_KG_BUFF_SCALED:COMPOUNDGLYPHOSATE
## DIETsmall_animals:HIGH_KG_BUFF_SCALED:COMPOUNDGLYPHOSATE
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for poisson family taken to be 1)
##
## Null deviance: 501735 on 62855 degrees of freedom
## Residual deviance: 43072 on 62801 degrees of freedom
## AIC: 254471
##
## Number of Fisher Scoring iterations: 4coefplot2(glm_mod_6)